When we consider earlier technology waves, there is often one company (or possibly two) that emerges as a leader and becomes synonymous with the technology. Mainframes became synonymous with IBM, virtualization spawned VMware, open source gave us Red Hat, web search was reinvigorated by Google, and social networks gave us Facebook and Twitter. We would argue that this has yet to happen with AI and machine learning (ML). AI – and its variants, notably machine learning – is an enabling technology. It enables computers to do things that weren't possible in earlier programming paradigms. With modern cloud computing, networking and storage, those things can be done at a scale unimaginable when the term'artificial intelligence' was first coined in the mid-1950s.

Machine Learning (ML) algorithms leverage large datasets to determine patterns and construct meaningful recommendations. Likewise, credit risk modelling is a field with access to a large amount of diverse data where ML can be deployed to add analytical value. In the following analysis, we explore how various ML techniques can be used for assessing probability of default (PD) and compare their performance in a real-world setting. A recent publication by the Bank of England (BoE) and the Financial Conduct Authority (FCA) reports the results of a survey on the use of ML in United Kingdom (UK) financial services.[1] Results show that two-thirds of respondents use ML in some form.

The system's calling card is a massive chip, measuring 8.5 inches by 8.5 inches. Putting the neural network on the chip, instead of dispersing it across a system, enables problems to be solved faster. Data travels a shorter distance, speeding up the processing of information. "You've got a chip that's about 60 times bigger than any existing chip. That raw capability is what we're trying to exploit," said Rick Stevens, associate laboratory director for computing, environment and life sciences at Argonne. When the AI computer was installed in November, originally for cancer research, the lab determined its computing power was almost equivalent to that of a cluster of computers with up to 300 graphics chips, which are widely used for AI.

IBM has seen the greatest adoption of artificial intelligence systems across industries in customer service applications. Clear, transparent and explainable outcomes are needed for AI systems to succeed widely. The financial services, retail, manufacturing, energy and utilities sectors are leading the business adoption of AI. Ruchir Puri, chief scientist at IBM Research, recently spoke to S&P Global Market Intelligence about developing trusted AI systems and how IBM is educating companies about ethical uses of the technology. Until February, Puri served as CTO and chief architect of supercomputer IBM Watson.

When the United Kingdom voted to leave the European Union in June, ultimately tanking the British pound, traders with access to Cambridge, Massachusetts-based artificial intelligence platform Kensho had a special advantage. With a few keystrokes on Kensho's AI-powered platform, traders quickly combed through an intelligence-grade database of information and in seconds learned that populist votes such as Brexit historically led to an extended drop in the local currency, washing out any short-term recovery. That's exactly what happened in the days and months after Brexit. The pound plunged to three-decade lows in July, sinking to $1.28 versus the dollar, before rallying slightly to $1.33. The currency has been in a slump since then and currently sits at $1.24.

When the United Kingdom voted to leave the European Union in June, ultimately tanking the British pound, traders with access to Cambridge, Massachusetts-based artificial intelligence platform Kensho had a special advantage. With a few keystrokes on Kensho's AI-powered platform, traders quickly combed through an intelligence-grade database of information and in seconds learned that populist votes such as Brexit historically led to an extended drop in the local currency, washing out any short-term recovery. That's exactly what happened in the days and months after Brexit. The pound plunged to three-decade lows in July, sinking to $1.28 versus the dollar, before rallying slightly to $1.33. The currency has been in a slump since then and currently sits at $1.24.

A file photo dated 06 January 2004 shows Apple Computer's CEO Steve Jobs sitting in front of a project Apple Computer logo during his keynote speech at the MacWorld Expo in San Francisco, California, USA. Apple's (AAPL) epic fall on the stock market took a symbolic turn Thursday after its market value again dropped below its top rival's. That makes it second fiddle behind Google parent Alphabet (GOOGL) at 499.9 billion. Shares of Apple have been in freefall this year, dropping more than 14%, amid the company's disappointing first-quarter results. It's the second time this year Alphabet's market value has surpassed Apple's.

A file photo dated 06 January 2004 shows Apple Computer's CEO Steve Jobs sitting in front of a project Apple Computer logo during his keynote speech at the MacWorld Expo in San Francisco, California, USA. Apple's (AAPL) epic fall on the markets took a symbolic turn Thursday after its market value again dropped below its top rival's. That makes it second fiddle behind Google parent Alphabet (GOOGL) at 497.8 billion. Shares of Apple have been in freefall this year, dropping more than 14%, amid the company's disappointing first-quarter results. It's the second time this year that Alphabet's market value has surpassed Apple's.